Method for calculating an estimate of a time-varying physiological variable

ABSTRACT

A medical device performs a method for computing an estimate of a physiological variable. The method includes sensing a physiological signal and measuring an event of the physiological signal. The device initializes a value of a long-term metric of the event measurement, wherein the long-term metric corresponds to a time interval correlated to a response time of the physiological variable to changes in the event. The estimate of the long-term metric is updated in a memory of the medical device using a previous long-term metric and a current measurement of the event. The device detects a need for computing the physiological variable and computes an estimate of the physiological variable using the updated long-term metric.

TECHNICAL FIELD

The disclosure relates generally to medical devices and, in particular,to an apparatus and method for calculating an estimate of aphysiological variable.

BACKGROUND

Implantable medical devices (IMDs) such as pacemakers and implantablecardiovertor defibrillators (ICDs) typically include sensors formonitoring physiological signals from a patient, such as cardiacelectrodes for sensing cardiac electrogram (EGM) signals. Using thesensed signals, the IMD may be configured to compute measurementscorresponding to a patient's cardiac rhythm and use that information incomputing therapy parameters controlling an automatically deliveredtherapy, such as a cardiac pacing therapy, which may be a singlechamber, dual chamber or multi-chamber cardiac resynchronization therapy(CRT), anti-tachycardia pacing (ATP) or other pacing therapies.

In some examples, control parameters determined from a sensed signal maybe computed from measurements of a physiological variable taken over arelatively short period of time, such as one cardiac cycle or severalcardiac cycles. In other examples, a parameter may be desired whichrequires computation from a physiological variable that requiresmeasurements over a relatively longer period of time, such as oneminute, several minutes, hours, weeks or more. A relatively large amountof IMD memory is required for storing signal data as it is acquired foruse in computing of a physiological variable using long-termmeasurements of a physiological signal. Computing a time varyingphysiological variable from signal measurements may require complexcomputational methods, such as differential, exponential, logarithmic orother non-linear functions, which require generally high processingpower and time. Computation of therapy control parameters usingmeasurements obtained over long time periods and/or requiring highcomputational complexity can be limited in an IMD because of the overallsize limitations, which limits battery size, memory size, and processingpower. A need remains, therefore, for IMD systems and associated methodsthat provide efficient computation of an estimated or predicted value ofa physiological variable for use in setting a therapy control parameterand/or for assessing a patient condition.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an implantable medical device system 8according to one embodiment.

FIG. 2 is a functional block diagram of the IMD shown in FIG. 1according to one embodiment.

FIG. 3 is a flow chart of a method for computing a therapy controlparameter according to one embodiment.

FIG. 4 is a flow chart of one method for computing a therapy controlparameter according to an illustrative embodiment.

FIG. 5 is a flow chart of a method for controlling a drug therapy usingthe estimation techniques described herein.

DETAILED DESCRIPTION

In the following description, references are made to illustrativeembodiments. It is understood that other embodiments may be utilizedwithout departing from the scope of the disclosure.

FIG. 1 is a schematic diagram of an implantable medical device system 8according to one embodiment. System 8 is provided for sensing cardiacevents (e.g. P-waves and R-waves) for detecting and classifying acardiac rhythm and for treating cardiac arrhythmias. System 8 includesIMD 10 and leads 20 and 21. IMD 10 may be embodied as an ICD capable ofdelivering pacing, cardioversion and defibrillation therapy to the heart16 of a patient 14. Ventricular lead 20 and atrial lead 21 areelectrically coupled to IMD 10 and extend into the patient's heart 16via a vein 18. Ventricular lead 20 includes electrodes 22 and 24 shownpositioned in the patient's right ventricle (RV) for sensing ventricularEGM signals and for delivering pacing pulses in the RV. Atrial lead 21includes electrodes 26 and 28 positioned in the patient's right atrium(RA) for sensing atrial EGM signals and delivering pacing pulses in theRA. Lead 20 additionally carries high voltage coil electrodes 42 and 44used to deliver cardioversion and defibrillation shock pulses.

The leads 20 and 21 are used to acquire intracardiac EGM signals fromthe patient 14 and to deliver therapy in response to the acquired data.IMD 10 is shown as a dual chamber ICD, but in some embodiments, system 8may be embodied as a single chamber system or as a multi-chamber systemincluding a coronary sinus lead extending into the right atrium, throughthe coronary sinus and into a cardiac vein to position electrodes alongthe left ventricle (LV) for sensing LV EGM signals and delivering pacingpulses to the LV.

IMD circuitry configured for performing the methods described herein andassociated battery(ies) are housed within a sealed housing 12. Housing12 may be conductive so as to serve as an electrode for use as anindifferent electrode during pacing or sensing or as an active electrodeduring defibrillation. As such, housing 12 is also referred to herein as“housing electrode” 12.

EGM signal data, cardiac rhythm episode data, and therapy delivery dataacquired by IMD 10 can be transmitted to an external device 30. Externaldevice 30 may be embodied as a programmer, e.g. used in a clinic orhospital to communicate with IMD 10 via wireless telemetry. Externaldevice 30 may be coupled to a remote patient monitoring system, such asCarelink®, available from Medtronic, Inc., Minneapolis, Minn.. Device 30is used to program commands or operating parameters into IMD 10 forcontrolling IMD function and to interrogate IMD 10 to retrieve data,including device operational data as well as physiological dataaccumulated in IMD memory. Examples of communication techniques used byIMD 10 and external device 30 include low frequency or radiofrequency(RF) telemetry, which may be an RF link established via Bluetooth, WiFi,or MICS, for example.

The techniques disclosed herein are useful in IMD system 8, for example,in computing pacing control parameters such as anti-tachycardia pacing(ATP) interval used to control pacing delivered to terminate ventricularor atrial tachycardia. System 8 is one example of an IMD system used toacquire physiological signals, in this example cardiac EGM signals, formeasuring physiological variables that are used to set adjustabletherapy control parameters to a value computed based on the measuredphysiological variables in a closed-loop, automatic therapy deliverysystem. However, the techniques disclosed herein may be implemented inany medical device system that senses physiological signals for use incalculating and reporting a time-varying physiological variable or forcalculating a physiological variable used to compute a therapy deliverycontrol parameter. Such systems may include but are not limited tocardiac monitors, respiration monitors, drug delivery devices, andneurostimulation devices. Systems employing the techniques describedherein may be wholly external systems or may include implantable devicesas shown in the illustrative embodiments.

FIG. 2 is a functional block diagram of IMD 10 according to oneembodiment. IMD 10 includes a sensing module 102, a therapy deliverymodule 104, a control unit 106 and associated memory 108, and telemetrymodule 118. As used herein, the term “module” refers to an applicationspecific integrated circuit (ASIC), an electronic circuit, a processor(shared, dedicated, or group) and memory that execute one or moresoftware or firmware programs, a combinational logic circuit, or othersuitable components that provide the described functionality.

Sensing module 102 receives cardiac electrical signals from electrodescarried by leads 20 and 21 for sensing cardiac events attendant to thedepolarization of myocardial tissue, e.g. P-waves and R-waves. Sensingmodule 102 may include a switch module for selectively couplingelectrodes 22, 24, 26, 28, 42, 44, and housing electrode 12 to sensingmodule 102 in order to monitor electrical activity of heart 16. Theswitch module may include a switch array, switch matrix, multiplexer, orany other type of switching device suitable to selectively coupleelectrodes to sensing module 102. In some examples, control unit 106selects the electrodes to function as sense electrodes, or the sensingvector, via the switch module within sensing module 102.

Sensing module 102 may include multiple sensing channels, each of whichmay be selectively coupled to respective combinations of electrodes 22,24, 26, 28, 42, 44 and housing 12 to detect electrical activity of aparticular chamber of heart 16, e.g. an atrial sensing channel and aventricular sensing channel. Each sensing channel may comprise a senseamplifier that outputs an indication to control unit 106 in response tosensing of a cardiac depolarization, in the respective chamber of heart16. In this manner, control unit 106 may receive sense event signalscorresponding to the occurrence of sensed R-waves and P-waves in therespective chambers of heart 16. Sensing module 102 may further includedigital signal processing circuitry for providing control unit 106 withdigitized EGM signals, which may be used for cardiac rhythmdiscrimination and for computing physiological variable values foradjusting therapy control parameters.

Sensing module 102 is shown coupled to electrodes for sensing cardiacEGM signals in FIG. 2. In other embodiments, however, electrodes forsensing other neurological signals and/or other physiological sensorsmay be coupled to sensing module 102. Other physiological sensors mayinclude pressure sensors, oxygen sensors, pH sensors or other bloodchemistry sensors, accelerometers, acoustical sensors, impedancesensors, flow sensors, or any other sensors used for acquiringphysiological signals over time.

Any physiological signals received by sensing module 102 and acquired byIMD 10 may be used in the techniques described herein for estimating aphysiological variable that varies in response to a changingphysiological condition(s), monitored by sensing module 102. In anillustrative embodiment described herein, myocardial action potentialduration (APD) varies with heart rate, i.e. RR interval. The faster theheart rate (shorter RRI), the shorter the APD, and the slower the heartrate (longer RRI) the longer the APD. However this variation of APD doesnot occur suddenly with changes in RRI but changes differentially towarda stable setpoint over time. The APD can be accurately computed as thesolution of a finite difference equation which is a function of theRRIs. This accuracy, however, is at the cost of increased memoryrequirements for storing RRIs over an extended period of time, e.g. oneminute or more, or of a higher processing demand and time forcomputations.

By using a hierarchy of computationally simpler metrics, for example asimple metric of the long-term RRI behavior, and a perhaps moresophisticated metric of the medium and/or short term RRI behavior, agood approximation of the differential behavior of the APD based onheart rate history can be computed with reduced cost. This approximationcould require negligible processing time and substantially reducedmemory. The approximation can be computed on demand when a need fortherapy is detected without requiring continuous computations when atherapy is not needed.

Techniques described herein enable efficient data storage andcomputational methods for estimating a physiological variable, e.g. APD,based on a time-varying physiological condition or repeating events,e.g. RRI. From the physiological variable, e.g. APD, a therapy controlparameter, e.g. a cardiac pacing interval, can be computed. In variousembodiments, the techniques described herein may be used for estimatingany physiological variable that is challenging to measure directly orinstantaneously for use in monitoring a patient or controlling atherapy. The variable may change in response to another physiologicalcondition in a non-linear manner. The variable therefore is estimatedusing measurements of at least one time-varying physiological conditionor repeating event that influences the variable over time and which canbe measured from signals sensed by sensing module 102.

Memory 108 may include computer-readable instructions that, whenexecuted by control unit 106, cause IMD 10 to perform various functionsattributed throughout this disclosure to IMD 10 and control module 106.The computer-readable instructions may be encoded within memory 108.Memory 108 may comprise computer-readable storage media including anyvolatile, non-volatile, magnetic, optical, or electrical media, such asa random access memory (RAM), read-only memory (ROM), non-volatile RAM(NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory,or any other digital media.

Control unit 106 may include any one or more of a microprocessor, acontroller, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), orequivalent discrete or integrated logic circuitry or state machine. Insome examples, control unit 106 may include multiple components, such asany combination of one or more microprocessors, one or more controllers,one or more DSPs, one or more ASICs, or one or more FPGAs, as well asother discrete or integrated logic circuitry or state machines. Thefunctions attributed to control unit 106 herein may be embodied assoftware, firmware, hardware or any combination thereof.

Control unit 106 includes a therapy control unit that controls therapydelivery module 104 to deliver electrical stimulation therapy, e.g.,cardiac pacing, anti-tachyarrhythmia therapy, or shock pulses, to heart16 according to a selected one or more therapy programs, which may bestored in memory 108. Therapy delivery module 104 is electricallycoupled to electrodes 22, 24, 26, 28, 42, 44 and housing electrode 12(all of which are shown in FIG. 1). Therapy delivery module 104 isconfigured to generate and deliver electrical stimulation therapy toheart 16 via selected combinations of electrodes 22, 24, 26, 28, 42, 44,and housing electrode 12.

Memory 108 stores intervals, counters, or other data used by controlunit 106 to control the delivery of pacing pulses by therapy deliverymodule 104. Such data may include intervals and counters used by controlunit 106 to control the delivery of pacing pulses to heart 16. Theintervals and/or counters are, in some examples, used by control unit106 to control the timing of delivery of pacing pulses relative to anintrinsic or paced event in another chamber. Memory 108 also storesintervals for controlling cardiac sensing functions such as blankingintervals and refractory sensing intervals and counters for countingsensed events for detecting cardiac rhythm episodes. Events sensed bysense amplifiers included in sensing module 102 are identified in partbased on their occurrence outside a blanking interval and inside oroutside of a refractory sensing interval. Events that occur withinpredetermined interval ranges are counted for detecting cardiac rhythms.

Memory 108 is additionally used to store data used for computing anestimate of a physiological variable and optionally for computing atherapy control parameter using the physiological variable. According toone embodiment described herein, sensing module 102, memory 108, andcontrol unit 106 are configured to measure a current value of arepeating physiological event or condition, use the current value toupdate a long-term metric of the physiological condition, and store thelong-term metric of the condition. At a time that therapy is needed, asdetermined by control unit 106 based on sensed signals from sensingmodule 102, the long-term metric is used to compute an estimate of aphysiological variable that varies in response to changes in thephysiological condition. A therapy control parameter is then computedbased on the estimated physiological variable. Next, the therapy controlparameter is applied by control unit 106 to control therapy delivered bytherapy delivery module 104.

Telemetry module 118 is used to communicate with external device 30, fortransmitting data accumulated by IMD 10 and for receiving interrogationand programming commands from external device 30. Examples ofcommunication techniques used by IMD 10 include low frequency orradiofrequency (RF) telemetry, which may be an RF link established viaBluetooth, WiFi, or MICS. IMD 10 receives programming commands andalgorithms from an external device via telemetry module 118.

FIG. 3 is a flow chart 200 of a method for computing a therapy controlparameter according to one embodiment. Flow chart 200 and other flowcharts presented herein are intended to illustrate the functionaloperation of the medical device system, and should not be construed asreflective of a specific form of software or hardware necessary topractice the methods described. Methods described in conjunction withflow charts presented herein may be implemented in a non-transitorycomputer-readable medium that includes instructions for causing aprogrammable processor to carry out the methods described. Anon-transitory computer-readable medium includes but is not limited toany volatile or non-volatile media, such as a RAM, ROM, CD-ROM, NVRAM,EEPROM, flash memory, or other computer-readable media, with the soleexception being a transitory, propagating signal. The instructions maybe implemented as one or more software modules, which may be executed bythemselves or in combination with other software.

At block 201, an initial estimate of a long-term metric of aphysiological condition is established. A long-term metric may be anystatistical metric of measurements of a physiological condition. Forexample, a long-term metric may be a mean (average), median, mode orother measurement of the central tendency of a distribution ofmeasurements of the physiological condition obtained from a sensedsignal. The initialized estimate of the long-term metric is stored inmemory 108. Examples of physiological conditions that may be measuredinclude, but are not limited to, cardiac conditions, metabolicconditions and respiratory conditions. The measurement of the conditionmay be an amplitude, a time interval, a slope, an integral, a dose, awaveform morphology, or other aspect of the physiological signal.

The physiological condition may be any event for which repeatedmeasurements are obtained and may be a cyclical event, such as a cardiacevent, respiratory event, or circadian event. To illustrate, a repeatingevent may be an RR interval (RRI) measured as the interval betweenR-waves consecutively sensed from a cardiac EGM signal. An initiallong-term estimate of a central tendency or “centeredness” of the RRIdistribution may be an average RRI initialized as a currently measuredRRI, a short-term average, e.g. a five to ten beat average, or a nominalRRI value.

At block 202, the physiological signal used to measure the physiologicalcondition is sensed by the IMD. The next measurement of thephysiological event is performed at block 204. This measurement may beof the next occurrence of a repeating event. At block 206, the long-termestimate of the metric of the physiological condition is updated usingthe next measurement and the new long-term estimate is stored in memory108. The actual value of the current event measurement does not need tobe accumulated in memory 108. In contrast to methods that accumulatemeasurements of a physiological condition or repeating event over timeto enable computation of a long-term average or other measure ofcenteredness of the repeating measurements, only the updated long-termestimate needs to be stored at block 206, significantly reducing memoryrequirements.

The “long-term” metric refers to a metric correlated to the centraltendency of the physiological condition over a period of timecorresponding to a response time of a physiological variable to changesin the condition. An example of this relation is the APD that changesdifferentially with changes in RR interval. The response time of APD tochanges in RR interval may be on the order of approximately one minutesuch that a long-term metric of RR interval is intended to correlate toa one-minute average RR interval in one embodiment, without having tostore one minute of RR interval measurements.

At block 208, the IMD control unit 106 determines if a therapy isneeded. This determination may be made according to any detectionalgorithm implemented in the IMD 10 for detecting a need for therapy.Examples include cardiac event interval tachyarrhythmia detectionalgorithms, which may utilize a prioritized set of rules, eventpatterns, signal morphology criteria or other detection criteria inaddition to measurements and counts of RR and/or PP, and PR intervalsfor detecting tachyarrhythmias.

If no condition or episode is detected by the IMD requiring therapy atblock 208, the control unit 106 continues to measure the physiologicalcondition at block 204 and updating the long-term estimate of thephysiological condition at block 206, which is stored in memory.

If therapy is needed (block 208), a physiological variable is computedat block 210 upon demand for use in controlling the therapy. Thephysiological variable is a variable that depends on the long-termbehavior of the physiological condition, which may be a repeating event,and therefore cannot be computed from only a currently measured value ora relatively short-term measurement of the physiological condition.Normally, a large amount of memory and processing would be required tocompute the physiological variable from a large amount of accumulatedevent measurements. Instead, upon determining that a therapy is needed,an estimate of the physiological variable is computed using thelong-term metric of the physiological condition. Additionally a currentvalue of the condition measurement and/or another short- or medium-termmeasurement of the condition may be used in the computation of thephysiological variable to estimate the variable as a function of thephysiological condition.

At block 212, a therapy control parameter that is automatically adjustedas a function of the physiological variable is computed by the controlunit 106. The therapy control parameter is applied by the therapycontrol portion of control unit 106. Therapy is applied according to thecontrol parameter at block 214. After delivering the therapy, theprocess may return to block 204 to continue measuring the physiologicalevent and updating the long-term metric of the event.

In some cases, the therapy is an urgent therapy, such as atachyarrhythmia therapy, and accordingly minimum processing time forcomputing a therapy control parameter is desired. By storing a long-termmetric of the physiological condition needed to compute an estimate ofthe physiological variable, processing time for computing the therapycontrol parameter upon detecting a need for therapy is minimized,enabling prompt therapy delivery.

As will be described in an illustrative embodiment below, a pacinginterval used for delivering ATP in response to detecting a tachycardiaepisode may be computed as a function of an estimate of APD. In oneembodiment, the APD is estimated using a long-term metric of the RRI.Accordingly, upon detecting a need for ATP, the pacing interval can becomputed on demand using the long term metric of the RRI for estimatingthe APD, which in turn is used to compute the ATP interval. While theestimated APD may be somewhat less accurate than an action potentialduration computed using the full computational method of differentiallyprocessing all RRI measurements accumulated and stored over a long-terminterval, the efficiency gained in reducing processing time and memoryrequirements by using the updated long-term metric of RRIs provides amore feasible technique for implementation in an IMD system.

In some embodiments, the estimated physiological variable itself may bestored and reported as part of a monitoring algorithm. In this case, thephysiological variable estimate may be computed on a periodic basis andstored in memory 108 or on demand, e.g. in response to an interrogationcommand from programmer 30, and transmitted via telemetry module 118. Atherapy control parameter need not be computed in all applications ofthe disclosed techniques; in some embodiments an estimated physiologicalvariable is computed, from a long-term metric of a repeatingphysiological event or condition that influences the variable, forpatient monitoring purposes, without therapy delivery.

FIG. 4 is a flow chart 300 of one method for computing a therapy controlparameter according to an illustrative embodiment. At block 302, an EGMsignal is sensed. For example a ventricular EGM signal is sensed usingelectrodes 22 and 24. At bock 304, a current RRI is measured. Along-term metric of an average RRI is initialized to the current RRIvalue.

At block 306, the next RRI is measured. The next RRI is compared to apredefined physiological limit or range of RRIs at block 308. In oneembodiment, the RRI is required to be greater than a minimum RRI limit,for example greater than 150 ms. If the next RRI is not determined to bephysiological based on predetermined acceptability criteria, the nextRRI is measured at block 306. In some cases, non-cardiac noise oroversensing of signals that are not true R-waves may cause false R-wavedetections resulting in non-physiological RRI measurements. Thesenon-physiological RRI measurements are rejected for use in updating anestimate of the long-term average RRI.

The next RRI measurement that is determined to be physiological is used,along with the initialized long-term metric, to update the metric of thelong-term average RRI at block 310. An updated metric of a long-termaverage may be computed by the control unit 106 as a weighted sum of thestored metric of the long-term average and the currently measured RRI.In one embodiment, the metric is computed as:

LTAvg(updated)={(n−1)*LTAvg}/n+RRI/n

wherein LTAvg is the stored long-term average estimate, RRI is thecurrently measured RRI value, and n is a selected weighting factor. Theweighting factor n is chosen to be 256 in one embodiment. The weightingfactor may be selected based on a fastest event rate expected to occurover a time interval needed to accurately estimate the physiologicalvariable. In other words, the weighting factor may be established basedon an expected number of event measurements during a response time ofthe physiological variable to changes in the event. The long-term metricis correlated to a centeredness measurement of the event over theresponse time in one embodiment. Additionally, the weighting factor maybe selected to enable straightforward hardware or firmwareimplementation in fixed point math.

For example, the APD may be estimated with an acceptable accuracy basedon approximately one minute of RRI measurements. Approximately oneminute may include between 50 seconds and 70 seconds of measurements forexample. During a fast heart rate, the tachycardia rate may be greaterthan 200 bpm, normally requiring storage of the 200 or more RRIs todetermine an average RRI from which the APD could be computed. Insteadof requiring this large amount of data storage, only the long-termmetric of average RRI is stored and a currently measured RRI is used toupdate the metric. A maximum event rate in an expected time intervalrequired to estimate APD with acceptable accuracy may be set at a rategreater than approximately 200 bpm, corresponding to an expected maximumventricular rate during the one minute time interval used to estimateAPD. In the illustrative embodiment, a maximum event rate is selected as256, since this rate is close to an expected maximum ventricular rate ifVT is detected, is computationally convenient to use inhardware/firmware implementations, and provides an acceptable resolutionof a final APD estimate.

This established maximum event rate is used to set the weighting factorapplied to the long-term metric and the currently measured RRI. Aweighting of 255 is applied to the long-term metric and a weighting ofone is applied to the currently measured RRI in computing the updatedmetric of long-term average RRI over an assumed maximum number ofevents, i.e. 256. For example, for computing the metric of the long-termaverage RRI the following equation may be used:

LTAvg(updated)={(255)*LTAvg}/256+RRI/256

The above equation may be generalized to a long-term average metric ofany repeated event measure where the long-term average (or othercenteredness) metric is updated as a sum of a weighted combination ofthe stored long-term average metric and a current measurement of therepeating event. The weighting factor may be chosen based on modeling ofreal clinical data to provide a best approximation of the actuallong-term average computed from accumulated measurements of a repeatingevent, e.g. RRIs in the illustrative embodiment. For example, theweighting factor applied to a current event measurement may be higher ifa greater dependency on very recent events compared to less recenthistorical events is observed.

The updated long-term average metric is stored in memory 108 at block310. The long-term average metric is updated in memory 108 continuouslyupon each RRI measurement such that every RRI measured (withinphysiological limits) contributes to the calculation of the historical,long-term average metric. Being continuously updated upon eachmeasurement means that the number of RRI measurements contributing tothe long-term average metric is not limited to a predetermined number ofRRIs and RRIs need not be counted for computing the long-term average.It is recognized that the metric of the long-term average could bereinitialized, for example by a clinician using programmer 30, whichwould restart computation of the long-term metric, effectively clearingthe contribution of historical RRIs to the metric.

A relatively shorter, medium term metric of recent RRIs is computed andstored at block 312. This medium term measure may be a running average,a mean, median, mode or other measure of a predetermined number of themost recent, consecutively measured repeating events, e.g. RRIs(rejecting non-physiological RRIs) in the illustrative embodiment. Forexample, the median RRI of the most recent 10 or 12 RRIs may bedetermined and stored at block 312. Generally, the medium term measurecould be computed as a mean or median value of a predetermined number ofthe most recently measured and stored event measurements, but any momentwith mathematically desirable properties could be used, and the momentcould in general be chosen based on the characteristics of thedifference equation being solved.

The number of recent event measurements used to compute the medium termmetric may be based on a minimum number of events expected to occur overthe time interval required to estimate APD with acceptable accuracy. Forexample, a minimum heart rate during one minute may be expected to beapproximately 40 beats per minute. If the weighting of the long-termaverage is high, e.g. based on a maximum event rate of 256, but theheart rate is relatively slow or near a minimum expected rate, thelong-term metric is heavily weighted on historical RRI values thatextend well-beyond the one minute time interval over which RRI changesare expected to impact APD. RRIs occurring more than one minute earlierwould have little or no influence on the behavior of the current APD.Accordingly, an estimate of the APD computed using a weighted long-termaverage metric can be corrected for error arising when a heart rate isslower than a maximum expected rate by determining and appropriatelyweighting a medium term metric in a computation of APD. A weightedcontribution of the medium term metric will be used to offset along-term metric heavily weighted on historical RRIs in computing thefinal APD estimate.

In one embodiment, a median value of the most recent 12 RRIs isdetermined at block 312, and updated beat by beat. This median valuerequires storage of only 12 RRIs, which generally does not require anexcessive amount of memory and is not computationally burdensome tocompute. The number of most recent events used to compute a medium termmetric may be based on the minimum number of events expected over a timeinterval required to make event measurements for computing an acceptablyaccurate estimate of a physiological variable. In other words, thenumber of most recent events used to compute a medium-term metric may bebased on a minimum number of event measurements expected to occur in theresponse time of the physiological variable to a change in the event.The number of most recent events used may additionally be adjusted fromthis expected minimum number of events based on available memorycapacity, computational convenience, and acceptable accuracy of theestimated APD, which may be based on comparisons to empiricalmeasurements.

The long-term RRI metric and the medium term RRI measurement continue tobe updated beat-by-beat until a tachycardia is detected at block 314.The long-term metric is stored without having to accumulate RRImeasurements in memory 108 for computing a long-term average RRI. Onlythe current long-term metric is stored and is updated using a currentRRI measurement, resulting in substantial memory and processing savingsas opposed to storing all RRIs over a selected time interval, such asone minute, and computing an average of all of those RRIs. Computationof the medium term measure of RRIs does not require a large amount ofmemory or processing time since only a selected number of the mostrecent events are used. Accordingly, a predetermined number of recentlymeasured RRIs meeting the physiological interval requirement are storedin a memory buffer to enable computation of the medium-term RRImeasurement on a beat-by-beat basis.

If a tachycardia is detected at block 314, an estimated APD is computedat block 316 using the currently stored value for the long-term RRImetric, the medium term RRI metric, and a currently measured RRI. Theventricular refractory period changes as a function of heart rate(RRIs). In order to effectively terminate a detected tachycardia episodeusing anti-tachycardia pacing (ATP), the ATP pacing pulses must beproperly timed. A proper pacing interval will result in a pacing pulseshortly after the ventricular APD and associated physiologicalrefractory period but before the next intrinsic tachycardiadepolarization. An ATP therapy will fail to terminate a tachycardiaepisode when the ATP pulses occur at a time outside of the proper timeinterval. For example, an ATP therapy applied during the APD will failto capture the heart due to the physiological refractory period of themyocardial tissue. ATP pulses applied too close to intrinsic tachycardiadepolarizations, may accelerate the tachycardia leading to fibrillation.Therefore, the ATP interval may be set based on an estimate of the APDto properly time the pacing pulses just after physiological refractory.Setting the pacing interval of the ATP pulses based on an estimated APDincreases the ability of IMD 10 to terminate the detected tachycardia,reducing the likelihood that the tachycardia will degrade intofibrillation, reducing the number of pulses delivered to the patient'sheart 16, and increasing the battery life of IMD 10.

The differential behavior of the APD as a function of heart rate can beapproximated using a long-term, medium-term, and immediate measure ofRRIs. By properly weighting each of the long-term, medium-term andimmediate components, a close approximation of the behavior of the APDcan be achieved. In one embodiment, the APD is computed on demand atblock 314 using a weighted combination of the long-term RRI metriccurrently stored in memory, a medium-term RRI average currently storedin memory, and the currently measured RRI as follows:

APD=MAX (E, C*(S1*LTAvg+S2*MTmedian+S3*RRI _(current))+D)

wherein the values S1, S2 and S3 are scaling or weighting factorsapplied to each of the respective estimated long term average RRI metric(LTAvg), medium term median RRI (MTmedian), and currently measuredRRI_(current). C and D are constants. C, S1, S2, S3 and D are selectedto provide a best fit approximation of the differential behavior of theAPD curve as a function of RRI. C, S1, S2 and S3 can also be selected toprovide efficient implementation. In one embodiment, C, S2 and S3 areselected to be 0.25 and S1 is selected to be 0.5. C is a scaling factorthat converts the overall RRI metric computed using the long-term,medium term and current RRI metrics to the APD portion of the RRI. S1 isselected to give greater weight to the long-term metric than the mediumterm metric and the current RRI measurement. However the medium termmetric and the current RRI contributions offset any error due tocontributions of more distant past historical values.

D is an offset selected as approximately 130 ms, corresponding to aminimum APD and provides a safety margin to ensure pacing outside of thevulnerable period. E is a maximum for the system and is approximately350 ms in one example. In any given application, the equation used tocompute an estimate of a physiological variable may be determined byfitting estimated data to empirical measurements such that selection ofscaling factor(s), weighting factors, offset, and a maximum or minimumlimit provides acceptable accuracy and resolution of the estimate.

Using the estimated APD based on the metric of the long term RRI andmore immediate RRI measurements, a pacing interval for ATP is computedat block 318. The ATP interval may be computed by multiplying the APD bya constant and/or adding a constant to the computed APD. A maximum upperlimit to the ATP interval may be set and used in place of a computed ATPinterval if it exceeds the maximum limit. The computed ATP interval isused by the therapy control unit included in control unit 106 forcontrolling therapy delivery unit 104. The ATP therapy is delivered toheart 16 at the computed ATP interval. After delivering the therapy tosuccessfully terminate the detected tachycardia, the process returns toblock 306 to continue measuring RRIs on a beat-by-beat basis andupdating the long-term RRI metric and the medium term RRI metric.

In other examples, a physiological variable that may be estimated usingthe techniques disclosed herein may include any variable, e.g. thatrelating to cardiac function, respiratory function, neurologicalfunction, endocrine function, or digestive function, that variesnon-linearly in response to changes in a physiological event orcondition. A metric of the physiological event or condition isdetermined over different time intervals including at least onerelatively longer time interval. The long time interval metric isestimated using a weighted function of a previous long term estimate anda current measurement of the event or physiological condition. Thislong-term metric is combined with at least one metric measured orestimated over a relatively shorter time interval, for example a currentevent measurement and/or a metric of a predetermined number of mostrecent event measurements. The weighted combination of these metrics isused to approximate the non-linear behavior of the physiologicalvariable that depends on the behavior of physiological condition orevent over a given time interval.

An event or physiological condition measured repeatedly for estimatingthe long term metric may correspond to cyclical events such as circadianevents, respiratory cycles, or cardiac cycles or non-cyclical eventssuch as changes in body fluids, changes in activity, changes indigestive activity etc. The measurement of the event or physiologicalcondition may be an electrical, mechanical (e.g. pressure, acoustical,motion or activity), chemical (e.g. pH, oxygen saturation, or otherblood parameters), or optical measurement. The physiological variableestimate may be used for patient monitoring purposes and/or forcomputing a therapy control parameter as described herein.

FIG. 5 is a flow chart 400 of a method for controlling a drug therapyusing the estimation techniques described herein. If a drug is beingadministered in response to detecting a pathological event or worseningphysiological condition, at equal or varying time intervals and/ordosages, the drug in the patient's system will be present in variouspharmacokinetic states corresponding to the uptake characteristics ofthe drug, e.g. an exponential decay of each administered dosage.Historical dosages will have a smaller remaining effect than more recentdosages. Historical dosages could be estimated as long-term averageusing the techniques described above for computing a long-term metric ofdrug delivery. A new dosage could then be computed using a weightedcombination of a long-term metric of delivered dosages and a mediumand/or short term dosage measurement. In this example, the physiologicalvariable being estimated is a residual drug effect, and thephysiological condition or event used to estimate the physiologicalvariable is the drug administrations or dosages delivered over time. A“sensed physiological signal” in this example is therefore a measurementof the drug delivery, which may be sensed or tracked by the IMD.

In FIG. 5, a long-term metric of drug delivery is initialized to aselected value, which may be a currently delivered dosage, at block 402.Upon detecting a need for drug administration at block 404, a residualdrug metric is computed at block 406 using the long-term metric computedat block 406. A dosage is then computed at block 408 based on theresidual drug metric and delivered. Initially, a dosage may be anominal, minimal, or maximal dosage until enough dosages have beendelivered to compute a dosage based on a formula for calculated aresidual drug metric as described below. The dosage may be delivered inresponse to automatically detecting a pathological condition orworsening physiological condition based on sensed signals received by asensing module 102 of IMD 10. In some embodiments, the need for drugadministration may be detected as a patient or clinician command. Thedosage may be delivered automatically by a therapy delivery module 104including a drug delivery pump under the control of a control unit 106of IMD 10.

The currently delivered dosage is used to update the long-term metric atblock 410 by computing a weighted combination of the long-term metricand the current dosage. The weighted combination may be computed usingthe general formula given previously for a long-term metric, whichincludes a weighting of the previous long-term metric corresponding to amaximum number of dosages expected to occur during a decay period of thedrug. In other words, the weighting of the previous long-term metricused in computing an updated metric may be based on a maximum number ofdosages expected to be delivered during a response time over which thedrug would decay.

At block 412, a medium-term drug delivery metric may be computed and maybe an average of a predetermined number of the most recent administereddosages. The process then waits until a need for drug administration isdetected and computes a residual drug metric at block 406. The residualdrug metric may taken into account how long ago the most recent dosagewas delivered. A metric of residual drug effects may be computed basedon a weighted combination of the long-term metric, medium-term metric,and most recent delivered dosage. The metric of residual drug effect maybe computed according to a formula of the general form given above forestimating a physiological variable from metrics of a physiologicalcondition or event. In this case, the residual drug effect is computedfrom a long-term metric and medium term metric of drug delivery and amost recent dosage, each with appropriate weighting factors. At block408, a new dosage is computed based on the residual drug metric, whichmay include applying a scaling factor and offset to the residual drugeffect metric, along with a maximum dosage limit. The coefficients andother constants used in computing a dosage may be based on fittingempirical data with a resolution needed according to particular drugcharacteristics. In this way, an appropriate dosage may be computed tomaintain the patient within an acceptable total drug load without havingto compute complex half-life behavior of multiple dosages over time.

Thus, a medical device system and associated method for computing anestimate of a physiological variable have been presented in theforegoing description with reference to specific embodiments. It isappreciated that various modifications to the referenced embodiments maybe made without departing from the scope of the disclosure as set forthin the following claims.

1. A method for computing an estimate of a physiological variable in amedical device, the method comprising: enabling the medical device tosense a physiological signal; measuring an event of the physiologicalsignal; initializing a value of a long-term metric of the eventmeasurement, wherein the long-term metric corresponds to a time intervalcorrelated to a response time of the physiological variable to changesin the event; updating in a memory of the medical device the estimate ofthe long-term metric using a previous long-term metric and a currentmeasurement of the event; detecting a need for computing thephysiological variable; and enabling a processor of the medical deviceto compute an estimate of the physiological variable using the updatedlong-term metric.
 2. The method of claim 1, wherein the long-term metricis correlated to a centeredness measurement of the event over theresponse time.
 3. The method of claim 1, further comprising establishinga first weighting factor corresponding to the previous long-term metricand a second weighting factor corresponding to the current eventmeasurement, the first weighting factor established based on an expectednumber of event measurements during the response time of the variable toa change in the event; computing the estimate as a weighted combinationof the previous long-term metric and the current measurement of theevent.
 4. The method of claim 2, wherein the first weighting factorcorresponds to a maximum number of expected event measurements occurringduring the response time.
 5. The method of claim 1, further comprising:establishing a predetermined number of the event measurements forcomputing a medium-term metric of the event; computing the medium-termmetric using the predetermined number of measurements of the event;wherein computing the physiological variable comprises computing aweighted combination of the updated long-term metric and the medium-termmetric.
 6. The method of claim 5, wherein establishing a predeterminednumber of the event measurements comprises selecting a predeterminednumber based on a minimum number of event measurements expected to occurin the response time of the physiological variable to a change in theevent.
 7. The method of claim 1, wherein computing the physiologicalvariable estimate comprises approximating a non-linear response of thephysiological variable as a function of the event by computing aweighted combination of the updated long-term metric, a medium termmetric of the event computed from a predetermined number of recent eventmeasurements, and a current measurement of the event.
 8. The method ofclaim 1, wherein detecting the need for computing the physiologicalvariable estimate comprises detecting a need for therapy delivery. 9.The method of claim 8, further comprising: computing a therapy controlparameter using the physiological variable estimate; and controlling atherapy delivery unit to deliver the therapy using the computed therapycontrol parameter.
 10. The method of claim 1, wherein measuring theevent comprises measuring a cardiac cycle interval; the physiologicalvariable being an action potential duration computed using an updatedlong-term metric of cardiac cycle intervals.
 11. The method of claim 9,wherein detecting the need for therapy delivery comprises detecting atachycardia, the therapy control parameter being an anti-tachycardiapacing interval, the physiological variable being an action potentialduration computed using an updated long-term metric of cardiac cycleintervals, wherein controlling the therapy delivery unit comprisescontrolling the therapy delivery unit to deliver anti-tachycardia pacingpulses at the pacing interval computed using the estimated actionpotential duration.
 12. The method of claim 1, wherein measuring theevent comprises measuring a delivered dosage of a drug; thephysiological variable being a residual drug effect computed using anupdated long-term metric of delivered dosage.
 13. A medical device forcomputing an estimate of a physiological variable, comprising: a sensingmodule receiving a physiological signal and measuring an event of thephysiological signal; a memory for storing a long-term metric of theevent measurement; and a processor configured to: initialize a value ofthe long-term metric of the event measurement, wherein the long-termmetric corresponds to a time interval correlated to a response time ofthe physiological variable to changes in the event; updating in thememory the estimate of the long-term metric using a previous long-termmetric and a current measurement of the event; detecting a need forcomputing the physiological variable; and compute an estimate of thephysiological variable using the updated long-term metric.
 14. Thedevice of claim 13, wherein the long-term metric is correlated to acenteredness measurement of the event over the response time.
 15. Thedevice of claim 13, wherein the memory stores a first weighting factorcorresponding to the previous long-term metric and a second weightingfactor corresponding to the current event measurement, the firstweighting factor established based on an expected number of eventmeasurements during the response time of the variable to a change in theevent; the processor configured to compute the estimate of the variableas a weighted combination of the previous long-term metric and thecurrent measurement of the event.
 16. The device of claim 15, whereinthe first weighting factor corresponds to a maximum number of expectedevent measurements occurring during the response time.
 17. The device ofclaim 13, wherein the processor is further configured to: compute amedium-term metric using a predetermined number of measurements of theevent; wherein computing the physiological variable estimate comprisescomputing a weighted combination of the updated long-term metric and themedium-term metric.
 18. The device of claim 17, wherein thepredetermined number of the event measurements comprises is based on aminimum number of event measurements expected to occur in the responsetime of the physiological variable to a change in the event.
 19. Thedevice of claim 13, wherein computing the physiological variableestimate comprises approximating a non-linear response of thephysiological variable as a function of the event by computing aweighted combination of the updated long-term metric, a medium termmetric of the event computed from a predetermined number of recent eventmeasurements, and a current measurement of the event.
 20. The device ofclaim 13, wherein detecting the need for computing the physiologicalvariable estimate comprises detecting a need for therapy delivery. 21.The device of claim 20, further comprising: a therapy control unitcomputing a therapy control parameter using the physiological variableestimate; and a therapy delivery unit controlled by the control unit todeliver the therapy using the computed therapy control parameter. 22.The device of claim 13, wherein measuring the event comprises measuringa cardiac cycle interval; the physiological variable being an actionpotential duration computed using an updated long-term metric of cardiaccycle intervals.
 23. The device of claim 21, wherein detecting the needfor therapy delivery comprises detecting a tachycardia, the therapycontrol parameter being an anti-tachycardia pacing interval, thephysiological variable being an action potential duration computed usingan updated long-term metric of cardiac cycle intervals, wherein thetherapy delivery unit is controlled to deliver anti-tachycardia pacingpulses at the pacing interval computed using the estimated actionpotential duration.
 24. The device of claim 13, wherein measuring theevent comprises measuring a delivered dosage of a drug; thephysiological variable being a residual drug effect computed using anupdated long-term metric of delivered dosage.
 25. A non-transitorycomputer-readable medium comprising instructions which cause a controlunit of a medical device system to perform a method, the methodcomprising: enabling the medical device to sense a physiological signal;measuring an event of the physiological signal; initializing a value ofa long-term metric of the event measurement, wherein the long-termmetric corresponds to a time interval correlated to a response time ofthe physiological variable to changes in the event; updating in a memoryof the medical device the estimate of the long-term metric using aprevious long-term metric and a current measurement of the event;detecting a need for computing the physiological variable; and enablinga processor of the medical device to compute an estimate of thephysiological variable using the updated long-term metric.